Demographics

The figure below shows the FNIR at FPIR=0.01 (t = -0.196) for different demographic groups. The bars show 90% confidence intervals.

Dataset:	Operational Dataset 4th pull
Samples used:	Both eyes
Enrolled Population:	500K people
Enrollment Method:	One enrollment session per person

Some consolidation of demographic information was necessary to improve statistical power. Eye color was consolidated to either light (grey, blue, or green) or dark (brown or black). Some subjects were labeled as being neither male nor female. Meaningful results for these categories could not be obtained because their sample sizes are too small. For the same reason, results for races other than white and black are not shown. The precise definitions of race, sex, and eye color used here can be found in EBTS version 10.0.

Logistic Regression

This section models the relationship between FNIR and various demographic characteristics using logistic regression. The response variable is whether the search produces a false negative at an FPIR of 0.01. The precise logit relationship is

$\ell =\log \frac{p}{1-p}={\beta }_0+{\beta }_1\mathbf{Sex}+{\beta }_2\mathbf{Race}+{\beta }_3\mathbf{Eye\; color}+{\beta }_4\mathbf{Sex\; and\; Eye\; Color}+{\beta }_5\mathbf{Sex\; and\; Race}$

where p is the probability of a false negative and ℓ is the log likelihood ratio of the probability of a false negative.

n = 312,114
Tjur’s $R^2$ = 0.000067

Negative (blue) values mean the probability of a miss is decreased. Tjur’s $R^2$ metric is a measure of the goodness-of-fit that produces values between 0 and 1. Precisely, it is the difference between the mean predicted probability for true positives (0.036) minus the mean predicted probability for false negatives (0.04). Race, sex, and eye color are generally poor predictors of accuracy, so the value is typically low.

The model does not include any interactions between race and eye color because there were not enough cases of blacks with light eyes to produce meaningful results. Eye color was unavailable for some subjects so MICE was used to perform imputation.

Demographic breakdown of hits

Demographic breakdown of false positives

The figures below show the demographic composistion of false positives.